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Designing a Content Cluster

IN ONE SENTENCE
A content cluster exists so that every sub-query produced by query fan-out lands on a page written to answer it.
A content cluster is a hub page plus interlinked detail pages. It suits passage retrieval: the hub carries concepts and relationships while each detail page answers one sub-question fully, so sub-queries from query fan-out are more likely to hit one of them.
OUR POSITION
A cluster's value is coverage without overlap, not page count. Two pages on one intent compete, and an uncovered sub-question is simply forfeited — so designing a cluster starts with listing sub-questions, not deciding a page count.
Which page types belong in a cluster
The hub: what sub-questions this subject contains, how they relate, and links to each detail page.
Concept detail pages: one concept each, covering definition and position.
Operational detail pages: how to do one specific thing and how to judge it.
Measurement detail pages: how it is measured and where it is misread. The three types map to questions at different stages.
Two common design errors
Overlapping detail pages: two pages on one intent, competing. The test is writing out each page's target question — if one sentence covers both, merge them.
A hub with no detail pages: it mentions everything and resolves nothing, so it is not the best answer to any sub-query.
Judging the size
Yield in this category concentrates in the top ten — around 13 monthly clicks per keyword at positions 1–3 and near zero beyond 11 — so cluster size should follow from how many sub-questions can realistically reach the top ten, not from a target page count.
If the sub-question list yields eight real questions, the cluster is eight pages. Padding it to twenty makes the last twelve rephrasing.
Data behind this page
13.1 / 3.4 / 0.1 / ≈0
Monthly clicks per keyword at positions 1–3 / 4–10 / 11–20 / 21+
Source:Our keyword-level analysis of 4,074 non-branded keywords for a leading site in this category,2026-06
Sources
- [1]Optimizing your website for generative AI features on Google Search.Google Search Central.2026-05-15
Updated 2026-08-10